Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [2]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Downloading mnist: 9.92MB [00:08, 1.16MB/s]                            
Extracting mnist: 100%|██████████| 60.0K/60.0K [00:12<00:00, 4.97KFile/s]
Downloading celeba: 1.44GB [00:28, 50.5MB/s]                               
Extracting celeba...

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[3]:
<matplotlib.image.AxesImage at 0x7f7e7d9286a0>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [4]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[4]:
<matplotlib.image.AxesImage at 0x7f7e7c0ae160>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [5]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [6]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    real_input_images = tf.placeholder(dtype = tf.float32,
                                       shape = (None, image_width, image_height, image_channels),
                                       name = 'real_input_images')
    z_input = tf.placeholder(dtype = tf.float32, shape = (None, z_dim), name = 'z_input')
    lr = tf.placeholder(dtype = tf.float32, name = 'learning_rate')
    
    return real_input_images, z_input, lr

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [7]:
def discriminator(images, reuse=False, alpha = 0.2):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    with tf.variable_scope('discriminator', reuse = reuse):
        x1 = tf.layers.conv2d(images, 32, 5, strides = 2, padding = 'same', use_bias = False, activation = None)
        relu1 = tf.maximum(alpha * x1, x1)
        
        x2 = tf.layers.conv2d(relu1, 64, 5, strides = 2, padding = 'same', use_bias = False, activation = None)
        bn2 = tf.layers.batch_normalization(x2, training = True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        
        x3 = tf.layers.conv2d(relu2, 128, 5, strides = 2, padding = 'same', use_bias = False, activation = None)
        bn3 = tf.layers.batch_normalization(x3, training = True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        
        flat = tf.reshape(relu3, (-1, 4*4*128))
        logits = tf.layers.dense(flat, 1, activation = None)
        out = tf.sigmoid(logits)
        
    return out, logits

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [8]:
def generator(z, out_channel_dim, is_train=True, alpha = 0.2):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    with tf.variable_scope('generator', reuse = not is_train):
        x1 = tf.layers.dense(z, 7*7*256, use_bias = False, activation = None)
        x1 = tf.reshape(x1, (-1, 7, 7, 256))
        x1 = tf.layers.batch_normalization(x1, training = is_train)
        x1 = tf.maximum(alpha * x1, x1)
        
        x2 = tf.layers.conv2d_transpose(x1, 128, 5, strides = 2, padding = 'same', use_bias = False, activation = None)
        x2 = tf.layers.batch_normalization(x2, training = is_train)
        x2 = tf.maximum(alpha * x2, x2)
        
        x3 = tf.layers.conv2d_transpose(x2, 64, 5, strides = 2, padding = 'same', use_bias = False, activation = None)
        x3 = tf.layers.batch_normalization(x3, training = is_train)
        x3 = tf.maximum(alpha * x3, x3)
        
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides = 1, padding = 'same')
        out = tf.tanh(logits)
        
    return out

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [9]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real, reuse = False)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse = True)
    
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = d_logits_real, 
                                                                         labels = tf.ones_like(d_model_real)))
    
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = d_logits_fake, 
                                                                         labels = tf.zeros_like(d_model_fake)))
    
    d_loss = d_loss_real + d_loss_fake
    
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = d_logits_fake,
                                                                    labels = tf.ones_like(d_model_fake)))

    return d_loss, g_loss

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [10]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1).minimize(d_loss, var_list = d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1).minimize(g_loss, var_list = g_vars)
        
    return d_train_opt, g_train_opt

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [11]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [12]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    real_input_images, z_input, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(real_input_images, z_input, data_shape[3])
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    steps = 0    
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                steps += 1
                
                batch_images *= 2
                
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                _ = sess.run(d_opt,
                             feed_dict={real_input_images: batch_images, z_input: batch_z, lr: learning_rate})
                _ = sess.run(g_opt,
                             feed_dict={real_input_images: batch_images, z_input: batch_z, lr: learning_rate})

                if steps % 100 == 0:
                    train_loss_d = d_loss.eval({real_input_images: batch_images, z_input: batch_z})
                    train_loss_g = g_loss.eval({real_input_images: batch_images, z_input: batch_z})
                    
                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    
                    show_generator_output(sess, 25, z_input, data_shape[3], data_image_mode)
                
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [19]:
batch_size = 16
z_dim = 128
learning_rate = 0.0008
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 1.4152... Generator Loss: 0.5150
Epoch 1/2... Discriminator Loss: 1.6321... Generator Loss: 0.3505
Epoch 1/2... Discriminator Loss: 1.7602... Generator Loss: 0.3113
Epoch 1/2... Discriminator Loss: 1.3084... Generator Loss: 0.5840
Epoch 1/2... Discriminator Loss: 1.3848... Generator Loss: 0.5322
Epoch 1/2... Discriminator Loss: 1.3547... Generator Loss: 0.8300
Epoch 1/2... Discriminator Loss: 1.6564... Generator Loss: 0.2823
Epoch 1/2... Discriminator Loss: 1.0400... Generator Loss: 0.8119
Epoch 1/2... Discriminator Loss: 1.4646... Generator Loss: 0.4992
Epoch 1/2... Discriminator Loss: 1.3332... Generator Loss: 0.4348
Epoch 1/2... Discriminator Loss: 1.1163... Generator Loss: 0.6981
Epoch 1/2... Discriminator Loss: 1.7714... Generator Loss: 0.2835
Epoch 1/2... Discriminator Loss: 1.3017... Generator Loss: 0.4747
Epoch 1/2... Discriminator Loss: 1.5645... Generator Loss: 0.3491
Epoch 1/2... Discriminator Loss: 1.2847... Generator Loss: 1.4470
Epoch 1/2... Discriminator Loss: 0.6241... Generator Loss: 1.6756
Epoch 1/2... Discriminator Loss: 1.0193... Generator Loss: 0.6022
Epoch 1/2... Discriminator Loss: 1.2180... Generator Loss: 0.5388
Epoch 1/2... Discriminator Loss: 1.1937... Generator Loss: 0.6899
Epoch 1/2... Discriminator Loss: 0.9104... Generator Loss: 0.8922
Epoch 1/2... Discriminator Loss: 0.9845... Generator Loss: 0.5893
Epoch 1/2... Discriminator Loss: 0.9481... Generator Loss: 0.9080
Epoch 1/2... Discriminator Loss: 0.8078... Generator Loss: 0.9858
Epoch 1/2... Discriminator Loss: 1.3253... Generator Loss: 0.5013
Epoch 1/2... Discriminator Loss: 1.2153... Generator Loss: 0.5256
Epoch 1/2... Discriminator Loss: 0.9133... Generator Loss: 0.7340
Epoch 1/2... Discriminator Loss: 0.4009... Generator Loss: 1.8789
Epoch 1/2... Discriminator Loss: 0.6982... Generator Loss: 1.5093
Epoch 1/2... Discriminator Loss: 0.4580... Generator Loss: 1.9532
Epoch 1/2... Discriminator Loss: 1.7574... Generator Loss: 0.3037
Epoch 1/2... Discriminator Loss: 1.2919... Generator Loss: 0.4849
Epoch 1/2... Discriminator Loss: 1.0975... Generator Loss: 0.5522
Epoch 1/2... Discriminator Loss: 0.3553... Generator Loss: 1.5686
Epoch 1/2... Discriminator Loss: 0.5406... Generator Loss: 1.5798
Epoch 1/2... Discriminator Loss: 0.7128... Generator Loss: 1.2893
Epoch 1/2... Discriminator Loss: 0.6587... Generator Loss: 1.0149
Epoch 1/2... Discriminator Loss: 0.4242... Generator Loss: 1.7363
Epoch 2/2... Discriminator Loss: 0.4279... Generator Loss: 1.8114
Epoch 2/2... Discriminator Loss: 0.8650... Generator Loss: 0.8600
Epoch 2/2... Discriminator Loss: 1.2746... Generator Loss: 0.4222
Epoch 2/2... Discriminator Loss: 0.8760... Generator Loss: 0.7124
Epoch 2/2... Discriminator Loss: 0.3284... Generator Loss: 1.7603
Epoch 2/2... Discriminator Loss: 0.8288... Generator Loss: 0.9138
Epoch 2/2... Discriminator Loss: 0.4570... Generator Loss: 1.5462
Epoch 2/2... Discriminator Loss: 0.4927... Generator Loss: 1.6593
Epoch 2/2... Discriminator Loss: 0.9076... Generator Loss: 0.6373
Epoch 2/2... Discriminator Loss: 0.5860... Generator Loss: 1.2301
Epoch 2/2... Discriminator Loss: 0.7214... Generator Loss: 1.2623
Epoch 2/2... Discriminator Loss: 0.5107... Generator Loss: 1.2914
Epoch 2/2... Discriminator Loss: 0.8413... Generator Loss: 0.9865
Epoch 2/2... Discriminator Loss: 0.3757... Generator Loss: 1.7316
Epoch 2/2... Discriminator Loss: 0.5415... Generator Loss: 1.2261
Epoch 2/2... Discriminator Loss: 1.4961... Generator Loss: 0.3987
Epoch 2/2... Discriminator Loss: 0.9083... Generator Loss: 0.7574
Epoch 2/2... Discriminator Loss: 0.7377... Generator Loss: 0.8677
Epoch 2/2... Discriminator Loss: 1.2339... Generator Loss: 1.1053
Epoch 2/2... Discriminator Loss: 0.5463... Generator Loss: 1.9145
Epoch 2/2... Discriminator Loss: 1.2132... Generator Loss: 0.5739
Epoch 2/2... Discriminator Loss: 0.9957... Generator Loss: 0.6614
Epoch 2/2... Discriminator Loss: 0.6813... Generator Loss: 0.9864
Epoch 2/2... Discriminator Loss: 0.4934... Generator Loss: 1.4787
Epoch 2/2... Discriminator Loss: 0.5557... Generator Loss: 1.6245
Epoch 2/2... Discriminator Loss: 0.8177... Generator Loss: 0.8432
Epoch 2/2... Discriminator Loss: 0.4856... Generator Loss: 2.5644
Epoch 2/2... Discriminator Loss: 0.3945... Generator Loss: 3.4535
Epoch 2/2... Discriminator Loss: 1.1026... Generator Loss: 0.5377
Epoch 2/2... Discriminator Loss: 1.2347... Generator Loss: 0.6079
Epoch 2/2... Discriminator Loss: 0.4906... Generator Loss: 1.4055
Epoch 2/2... Discriminator Loss: 0.6597... Generator Loss: 0.9547
Epoch 2/2... Discriminator Loss: 1.2892... Generator Loss: 0.8306
Epoch 2/2... Discriminator Loss: 0.4034... Generator Loss: 2.9371
Epoch 2/2... Discriminator Loss: 0.4028... Generator Loss: 1.9506
Epoch 2/2... Discriminator Loss: 2.0187... Generator Loss: 0.2405
Epoch 2/2... Discriminator Loss: 0.8327... Generator Loss: 1.7721
Epoch 2/2... Discriminator Loss: 0.5414... Generator Loss: 1.3445

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [20]:
batch_size = 16
z_dim = 128
learning_rate = 0.0008
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 3.0831... Generator Loss: 0.2571
Epoch 1/1... Discriminator Loss: 1.4362... Generator Loss: 0.3877
Epoch 1/1... Discriminator Loss: 2.0597... Generator Loss: 0.2285
Epoch 1/1... Discriminator Loss: 0.9689... Generator Loss: 1.2690
Epoch 1/1... Discriminator Loss: 1.3371... Generator Loss: 0.9392
Epoch 1/1... Discriminator Loss: 1.6371... Generator Loss: 0.4483
Epoch 1/1... Discriminator Loss: 1.8351... Generator Loss: 0.2386
Epoch 1/1... Discriminator Loss: 1.5636... Generator Loss: 0.6486
Epoch 1/1... Discriminator Loss: 1.3217... Generator Loss: 0.8002
Epoch 1/1... Discriminator Loss: 1.4292... Generator Loss: 0.5848
Epoch 1/1... Discriminator Loss: 1.6316... Generator Loss: 0.4704
Epoch 1/1... Discriminator Loss: 1.3909... Generator Loss: 0.4915
Epoch 1/1... Discriminator Loss: 1.4700... Generator Loss: 0.4688
Epoch 1/1... Discriminator Loss: 1.3512... Generator Loss: 0.6006
Epoch 1/1... Discriminator Loss: 1.5150... Generator Loss: 0.4005
Epoch 1/1... Discriminator Loss: 1.3221... Generator Loss: 0.6315
Epoch 1/1... Discriminator Loss: 1.6071... Generator Loss: 0.3896
Epoch 1/1... Discriminator Loss: 1.2117... Generator Loss: 0.6731
Epoch 1/1... Discriminator Loss: 1.3630... Generator Loss: 0.5716
Epoch 1/1... Discriminator Loss: 1.2852... Generator Loss: 0.7689
Epoch 1/1... Discriminator Loss: 1.4361... Generator Loss: 0.5142
Epoch 1/1... Discriminator Loss: 1.4746... Generator Loss: 0.5515
Epoch 1/1... Discriminator Loss: 1.3089... Generator Loss: 0.6663
Epoch 1/1... Discriminator Loss: 1.3454... Generator Loss: 0.7233
Epoch 1/1... Discriminator Loss: 1.6024... Generator Loss: 0.3709
Epoch 1/1... Discriminator Loss: 1.4170... Generator Loss: 0.5955
Epoch 1/1... Discriminator Loss: 1.2868... Generator Loss: 0.5684
Epoch 1/1... Discriminator Loss: 1.6024... Generator Loss: 0.4910
Epoch 1/1... Discriminator Loss: 1.5477... Generator Loss: 0.4217
Epoch 1/1... Discriminator Loss: 1.5115... Generator Loss: 0.3681
Epoch 1/1... Discriminator Loss: 1.4940... Generator Loss: 0.5208
Epoch 1/1... Discriminator Loss: 1.3485... Generator Loss: 0.5727
Epoch 1/1... Discriminator Loss: 1.5018... Generator Loss: 0.6811
Epoch 1/1... Discriminator Loss: 1.3585... Generator Loss: 0.5275
Epoch 1/1... Discriminator Loss: 1.5465... Generator Loss: 0.5034
Epoch 1/1... Discriminator Loss: 1.4630... Generator Loss: 0.4836
Epoch 1/1... Discriminator Loss: 1.2867... Generator Loss: 0.4336
Epoch 1/1... Discriminator Loss: 1.4172... Generator Loss: 0.4737
Epoch 1/1... Discriminator Loss: 1.3549... Generator Loss: 0.5348
Epoch 1/1... Discriminator Loss: 1.4509... Generator Loss: 0.4545
Epoch 1/1... Discriminator Loss: 1.2985... Generator Loss: 0.6335
Epoch 1/1... Discriminator Loss: 1.2664... Generator Loss: 0.5650
Epoch 1/1... Discriminator Loss: 1.5175... Generator Loss: 0.6297
Epoch 1/1... Discriminator Loss: 1.4344... Generator Loss: 0.5491
Epoch 1/1... Discriminator Loss: 1.5867... Generator Loss: 0.4157
Epoch 1/1... Discriminator Loss: 1.3276... Generator Loss: 0.6283
Epoch 1/1... Discriminator Loss: 1.3074... Generator Loss: 0.6090
Epoch 1/1... Discriminator Loss: 1.5146... Generator Loss: 0.5186
Epoch 1/1... Discriminator Loss: 1.4615... Generator Loss: 0.5334
Epoch 1/1... Discriminator Loss: 1.4671... Generator Loss: 0.6072
Epoch 1/1... Discriminator Loss: 1.5003... Generator Loss: 0.5200
Epoch 1/1... Discriminator Loss: 1.2981... Generator Loss: 0.6039
Epoch 1/1... Discriminator Loss: 1.4819... Generator Loss: 0.5151
Epoch 1/1... Discriminator Loss: 1.4028... Generator Loss: 0.5654
Epoch 1/1... Discriminator Loss: 1.4930... Generator Loss: 0.4630
Epoch 1/1... Discriminator Loss: 1.2393... Generator Loss: 0.7124
Epoch 1/1... Discriminator Loss: 1.3310... Generator Loss: 0.6299
Epoch 1/1... Discriminator Loss: 1.2987... Generator Loss: 0.6283
Epoch 1/1... Discriminator Loss: 1.0131... Generator Loss: 0.7692
Epoch 1/1... Discriminator Loss: 1.2365... Generator Loss: 0.6144
Epoch 1/1... Discriminator Loss: 1.2692... Generator Loss: 0.5754
Epoch 1/1... Discriminator Loss: 1.6552... Generator Loss: 0.4334
Epoch 1/1... Discriminator Loss: 1.5055... Generator Loss: 0.5436
Epoch 1/1... Discriminator Loss: 1.3513... Generator Loss: 0.6945
Epoch 1/1... Discriminator Loss: 1.3806... Generator Loss: 0.4928
Epoch 1/1... Discriminator Loss: 1.8925... Generator Loss: 0.3360
Epoch 1/1... Discriminator Loss: 1.2596... Generator Loss: 0.6351
Epoch 1/1... Discriminator Loss: 1.4778... Generator Loss: 0.5869
Epoch 1/1... Discriminator Loss: 1.5865... Generator Loss: 0.4899
Epoch 1/1... Discriminator Loss: 1.3640... Generator Loss: 0.6306
Epoch 1/1... Discriminator Loss: 1.3334... Generator Loss: 0.6234
Epoch 1/1... Discriminator Loss: 1.4406... Generator Loss: 0.5112
Epoch 1/1... Discriminator Loss: 1.4667... Generator Loss: 0.5549
Epoch 1/1... Discriminator Loss: 1.6862... Generator Loss: 0.4174
Epoch 1/1... Discriminator Loss: 1.4434... Generator Loss: 0.5604
Epoch 1/1... Discriminator Loss: 1.5200... Generator Loss: 0.5288
Epoch 1/1... Discriminator Loss: 1.3070... Generator Loss: 0.7442
Epoch 1/1... Discriminator Loss: 1.3298... Generator Loss: 0.6123
Epoch 1/1... Discriminator Loss: 1.4119... Generator Loss: 0.5960
Epoch 1/1... Discriminator Loss: 1.4328... Generator Loss: 0.5635
Epoch 1/1... Discriminator Loss: 1.2183... Generator Loss: 0.6037
Epoch 1/1... Discriminator Loss: 1.5735... Generator Loss: 0.4741
Epoch 1/1... Discriminator Loss: 1.3249... Generator Loss: 0.6926
Epoch 1/1... Discriminator Loss: 1.3554... Generator Loss: 0.6072
Epoch 1/1... Discriminator Loss: 1.4404... Generator Loss: 0.5174
Epoch 1/1... Discriminator Loss: 1.4528... Generator Loss: 0.6627
Epoch 1/1... Discriminator Loss: 1.4499... Generator Loss: 0.5604
Epoch 1/1... Discriminator Loss: 1.5963... Generator Loss: 0.5074
Epoch 1/1... Discriminator Loss: 1.4566... Generator Loss: 0.6114
Epoch 1/1... Discriminator Loss: 1.5884... Generator Loss: 0.5197
Epoch 1/1... Discriminator Loss: 1.4407... Generator Loss: 0.5561
Epoch 1/1... Discriminator Loss: 1.3515... Generator Loss: 0.5933
Epoch 1/1... Discriminator Loss: 1.4026... Generator Loss: 0.5802
Epoch 1/1... Discriminator Loss: 1.3544... Generator Loss: 0.6276
Epoch 1/1... Discriminator Loss: 1.3324... Generator Loss: 0.5914
Epoch 1/1... Discriminator Loss: 1.1452... Generator Loss: 0.7182
Epoch 1/1... Discriminator Loss: 1.4531... Generator Loss: 0.5535
Epoch 1/1... Discriminator Loss: 1.4082... Generator Loss: 0.5730
Epoch 1/1... Discriminator Loss: 1.4980... Generator Loss: 0.5517
Epoch 1/1... Discriminator Loss: 1.5000... Generator Loss: 0.5193
Epoch 1/1... Discriminator Loss: 1.4426... Generator Loss: 0.5843
Epoch 1/1... Discriminator Loss: 1.4455... Generator Loss: 0.6147
Epoch 1/1... Discriminator Loss: 1.4245... Generator Loss: 0.6164
Epoch 1/1... Discriminator Loss: 1.2823... Generator Loss: 0.6649
Epoch 1/1... Discriminator Loss: 1.3931... Generator Loss: 0.6568
Epoch 1/1... Discriminator Loss: 1.4859... Generator Loss: 0.5656
Epoch 1/1... Discriminator Loss: 1.4914... Generator Loss: 0.5443
Epoch 1/1... Discriminator Loss: 1.3954... Generator Loss: 0.6406
Epoch 1/1... Discriminator Loss: 1.4015... Generator Loss: 0.5722
Epoch 1/1... Discriminator Loss: 1.3393... Generator Loss: 0.6231
Epoch 1/1... Discriminator Loss: 1.3777... Generator Loss: 0.6069
Epoch 1/1... Discriminator Loss: 1.2385... Generator Loss: 0.7009
Epoch 1/1... Discriminator Loss: 1.5430... Generator Loss: 0.5185
Epoch 1/1... Discriminator Loss: 1.4293... Generator Loss: 0.5631
Epoch 1/1... Discriminator Loss: 1.5005... Generator Loss: 0.5550
Epoch 1/1... Discriminator Loss: 1.4960... Generator Loss: 0.5162
Epoch 1/1... Discriminator Loss: 1.4768... Generator Loss: 0.4771
Epoch 1/1... Discriminator Loss: 1.4363... Generator Loss: 0.5808
Epoch 1/1... Discriminator Loss: 1.4423... Generator Loss: 0.6381
Epoch 1/1... Discriminator Loss: 1.4284... Generator Loss: 0.6585
Epoch 1/1... Discriminator Loss: 1.4178... Generator Loss: 0.5907
Epoch 1/1... Discriminator Loss: 1.4660... Generator Loss: 0.5960
Epoch 1/1... Discriminator Loss: 1.4072... Generator Loss: 0.6401
Epoch 1/1... Discriminator Loss: 1.3868... Generator Loss: 0.6160
Epoch 1/1... Discriminator Loss: 1.3132... Generator Loss: 0.6061
Epoch 1/1... Discriminator Loss: 1.5503... Generator Loss: 0.5788

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.